The invention relates to a method for estimating fetal weight which comprises the following steps:
a) Ultrasonically measuring fetal biometric parameters, particularly fetal head, abdominal and limb dimensions of sample cases;
b) Determining further physiological and phenomenological pregnancy parameters of these sample cases;
c) Determining the fetal weight of the sample cases by precision weighing at birth;
d) Creating a database of these known sample cases, in which each record associated to each known case comprises the fetal biometric parameters, the additional physiological and phenomenological pregnancy parameters and the fetal weight as determined at birth;
d) Generating mathematical prediction models from the database of known cases;
e) Ultrasonically measuring said fetal biometric parameters, particularly fetal head, abdominal and limb dimensions of the cases under examination, for which fetal weight is to be predicted;
f) Determining further physiological and phenomenological pregnancy parameters of these cases under examination, for which fetal weight is to be predicted;
g) Predicting the fetal weight of said cases under examination by using the mathematical prediction models generated in step d.
Obstetric management of deliveries is affected by gestational age and fetal weight. At present, an accurate ultrasonographic examination, as well as the comparison of certain ultrasound biometric parameters of the fetus with respective normal curves, particularly before the 20th week of pregnancy, provides reliable gestation dating. However, many problems are still associated to fetal weight estimation by ultrasonic measurement of standard biometric parameters, usually relating to geometric head, abdominal and limb dimensions. In modern perinatology, fetal growth monitoring is of the utmost importance, for its being strictly related to fetus health. The detection of alterations in fetal growth allows to optimize pregnancy management.
Intensive research has been conducted for the last 30 years and led to the publication of many fetal weight estimation models from intrauterine ultrasound in specialized reviews. Most of these models are based on empirical mathematical formulas, determined from statistical regression methods, and only a few of them are based on physical principles; in recent times, a few models based on Artificial Neural Networks (ANN) have been documented.
Clinical use of these models has produced an increased number of ultrasound examinations which also specify fetal weight. Although these models are effective in original experiments, ordinary practical experience shows that the various formulas that have been proposed in the literature prove a considerably lower reliability in their clinical use.
The difference between the accuracy documented in the literature and that obtained in practical cases is likely to be caused by measurement procedures, model generation methods and sampling errors. More specifically, in most cases, statistical linear and non linear regression models are used, whose mathematical formulas contain parameters to be estimated by minimizing the quadratic error. The latter is evaluated by using ultrasound data acquired not more than six days before birth, which data may be associated to the real fetal weight, considered equal to the newborn weight, measured with precision scales. Estimates of model parameters are often based on a small number of data items relating to cases that do not wholly represent the entire population, and sometimes fetuses in a too homogeneous weight range are used.
Attempts to reduce the absolute error, by introducing correction factors in the algorithm, as well as new information such as the amount of amniotic fluid, the number of fetuses and any maternal pathologies, and new ultrasound biometric parameters differing from routinely measured parameters have produced no significant improvement. The new mathematical formulas still have the above drawbacks, and further require the use of unusual ultrasound biometric parameters, which often involve higher measurement difficulties, especially for operators having little experience.
In short, regardless of the method in use, in clinical practice human and instrumental measurement errors affect the accuracy of fetal weight estimation, and the average absolute error with respect to the real weight is never below 7-8%. While this value would be acceptable in itself, a high standard deviation causes more than a quarter of cases to be estimated with an error of more than 10% which value is often considered a threshold above which the estimation is deemed not to comply with a proper clinical use.
This makes the method unsuitable for effective assistance in clinical decision, especially due to increasingly frequent legal implications of a wrong medical decision. Furthermore, the error tends to increase when the estimation is aimed at diagnosing fetal macrosomia. It was found that, in case of abnormal fetal growth, the absolute error on fetal weight for the various models is often of more than 10-15%. Moreover, the precision of mathematical formulas of weight prediction decreases when weight estimation should be more accurate. In all proposed models, a generalized tendency is observed toward over/underestimation of weight of macro/microsomic fetuses. None of the proposed models can provide significantly better estimates than the other models. In practice, there exist about ten formulas which provide the best results with no significant differences therebetween.
Since most clinical problems relate to microsomic fetuses (below 2500 g) and to macrosomic fetuses (above 4000 g), mention should be also made to many specific models that have been developed in the literature with the purpose of providing more accurate estimates in these weight ranges. Although some of these specific models provide significantly lower errors for microsomic and macrosomic fetuses, they have the serious drawback of being only accurate for previously diagnosed microsomic or macrosomic fetuses. Such an a priori classification is not easy at all, especially for borderline cases, which are the most important cases from the clinical point of view. An error in this a priori classification involves the use of a totally unsuitable specific model, with a dramatic increase of the estimation error. This implies increased health care risks and heavier legal implications for operators.
More complex attempts to create models on partly overlapping weight ranges to obviate or at least attenuate the problem of a priori classification errors do not introduce actual improvements with respect to the use of a single model for all fetuses.
The conclusion is that the use of models for specific weight ranges is not convenient.
Therefore, the reliability of fetal weight estimates from ultrasound biometric measurements and pregnancy-related information is hitherto highly questionable, particularly for fetuses whose neonatal weight is situated on either side of normal distribution, i.e. the above mentioned microsomic and macrosomic fetuses.
However, since the accuracy of estimates is actually close to the threshold for useful use thereof in clinical decision, it is highly desirable to find effective solutions to reduce the error just as little as is sufficient to assure effectiveness of the method.
At present, birth weight prediction accuracy seems to be only improvable by basically trying to reduce the human error associated to the use of the ultrasound imaging apparatus to measure the main fetal dimensions in utero. Operators with a longer experience in the field of fetal ultrasound are known to provide a significantly lower error level than less expert operators, although the former still have unacceptable inaccuracy margins on particularly difficult cases.
This invention is based on the problem of improving a method of the type described hereinbefore so that the problems of the above prior art methods may be obviated without requiring heavier loads of computation and/or detection of ultrasound biometric and/or physiological and phenomenological pregnancy parameters.
Particularly, besides providing a more accurate fetal weight estimate, the invention has the object of providing probabilistic data on the reliability or consistency of the measurements of ultrasound biometric parameters and/or physiological and/or phenomenological pregnancy parameters with the actually obtained fetal weight estimate, to allow real-time corrections based on new measurements of such parameters.
The invention has the further object of providing a method as described hereinbefore, which may be easily managed by the operator and allow direct and immediate data reading.
Therefore, the invention proposes a method as described hereinbefore, in which fetal weight is predicted on the basis of sample case data, relating to ultrasound biometric parameters and/or physiological and/or phenomenological pregnancy parameters, by using a mathematical multivariate Gaussian probabilistic model.
The use of a probabilistic model provides a number of advantages: the main advantage, in the specific case of this invention, is to provide unbiased estimates, i.e. with no systematic over/underestimates of micro/macrosomic fetuses. Furthermore, the mathematical multivariate or multinormal model allows to associate probability levels to estimates, to evaluate both the reliability of the estimated weight, and the mutual consistency of the measurements of ultrasound biometric parameters and/or physiological and/or phenomenological pregnancy parameters.
The invention further relates to a system for determining the fetal weight, characterized in that it comprises a computer having at least one data input device and at least one display monitor and in which a computer program is or may be loaded, wherein the algorithms of the multinormal model, the interfaces for access to sample case database data, and the algorithms of the mean vector and covariance matrix estimation model, as well as the code for displaying the neonatal weight estimate and the probability thereof are coded.
Further characteristics and improvements will form the subject of the dependent claims.
The characteristics of the invention and the advantages derived therefrom will appear more clearly from the following description of a few embodiments, with reference to the annexed drawings.